Research of the Early Warning Model of Grape Disease and Insect Based on Rough Neural Network

Abstract : The grape is one of the four fruits in the world and its cultivation area and production has been ranked first in the world. The area is growing in our country after the reform and opening up which is significant for the rural economic development and farmers’ income. However, the growing of grape diseases and insect pests has become one of the important problems in the development of grape planting industry. In the paper, intensive and overall surveys and studies of the research progress of the early warning model of the disease and insect are made firstly and then the comparison and analysis of rough set and artificial neural network are presented. Finally, we collected a large amount of data from grape planting base of the grape and wine engineering technology research and development center in Beijing. Based on the real-time sensing data technologies of Internet of things and the intelligent grape early warning model based on rough neural network established in the paper, we did a number of experiments and its validity was verified. The model can provide beneficial references for the research of other crops diseases and insect pests.
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Communication dans un congrès
9th International Conference on Computer and Computing Technologies in Agriculture (CCTA), Sep 2015, Beijing, China. IFIP Advances in Information and Communication Technology, AICT-479 (Part II), pp.310-319, 2016, Computer and Computing Technologies in Agriculture IX. 〈10.1007/978-3-319-48354-2_32〉
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Dengwei Wang, Tian’en Chen, Chi Zhang, Li Gao, Li Jiang. Research of the Early Warning Model of Grape Disease and Insect Based on Rough Neural Network. 9th International Conference on Computer and Computing Technologies in Agriculture (CCTA), Sep 2015, Beijing, China. IFIP Advances in Information and Communication Technology, AICT-479 (Part II), pp.310-319, 2016, Computer and Computing Technologies in Agriculture IX. 〈10.1007/978-3-319-48354-2_32〉. 〈hal-01614172〉

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